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  <h1>Source code for openspeech.criterion.transducer.transducer</h1><div class="highlight"><pre>
<span></span><span class="c1"># MIT License</span>
<span class="c1">#</span>
<span class="c1"># Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho</span>
<span class="c1">#</span>
<span class="c1"># Permission is hereby granted, free of charge, to any person obtaining a copy</span>
<span class="c1"># of this software and associated documentation files (the &quot;Software&quot;), to deal</span>
<span class="c1"># in the Software without restriction, including without limitation the rights</span>
<span class="c1"># to use, copy, modify, merge, publish, distribute, sublicense, and/or sell</span>
<span class="c1"># copies of the Software, and to permit persons to whom the Software is</span>
<span class="c1"># furnished to do so, subject to the following conditions:</span>
<span class="c1">#</span>
<span class="c1"># The above copyright notice and this permission notice shall be included in all</span>
<span class="c1"># copies or substantial portions of the Software.</span>
<span class="c1">#</span>
<span class="c1"># THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span>
<span class="c1"># IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span>
<span class="c1"># FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span>
<span class="c1"># AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span>
<span class="c1"># LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span>
<span class="c1"># OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span>
<span class="c1"># SOFTWARE.</span>

<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">from</span> <span class="nn">omegaconf</span> <span class="kn">import</span> <span class="n">DictConfig</span>

<span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">register_criterion</span>
<span class="kn">from</span> <span class="nn">..transducer.configuration</span> <span class="kn">import</span> <span class="n">TransducerLossConfigs</span>
<span class="kn">from</span> <span class="nn">...utils</span> <span class="kn">import</span> <span class="n">WARPRNNT_IMPORT_ERROR</span>
<span class="kn">from</span> <span class="nn">...tokenizers.tokenizer</span> <span class="kn">import</span> <span class="n">Tokenizer</span>


<div class="viewcode-block" id="TransducerLoss"><a class="viewcode-back" href="../../../../modules/Criterion.html#openspeech.criterion.transducer.transducer.TransducerLoss">[docs]</a><span class="nd">@register_criterion</span><span class="p">(</span><span class="s2">&quot;transducer&quot;</span><span class="p">,</span> <span class="n">dataclass</span><span class="o">=</span><span class="n">TransducerLossConfigs</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">TransducerLoss</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Compute path-aware regularization transducer loss.</span>

<span class="sd">    Args:</span>
<span class="sd">        configs (DictConfig): hydra configuration set</span>
<span class="sd">        tokenizer (Tokenizer): tokenizer is in charge of preparing the inputs for a model.</span>

<span class="sd">    Inputs:</span>
<span class="sd">        logits (torch.FloatTensor): Input tensor with shape (N, T, U, V)</span>
<span class="sd">            where N is the minibatch size, T is the maximum number of</span>
<span class="sd">            input frames, U is the maximum number of output labels and V is</span>
<span class="sd">            the vocabulary of labels (including the blank).</span>
<span class="sd">        targets (torch.IntTensor): Tensor with shape (N, U-1) representing the</span>
<span class="sd">            reference labels for all samples in the minibatch.</span>
<span class="sd">        input_lengths (torch.IntTensor): Tensor with shape (N,) representing the</span>
<span class="sd">            number of frames for each sample in the minibatch.</span>
<span class="sd">        target_lengths (torch.IntTensor): Tensor with shape (N,) representing the</span>
<span class="sd">            length of the transcription for each sample in the minibatch.</span>

<span class="sd">    Returns:</span>
<span class="sd">        - loss (torch.FloatTensor): transducer loss</span>

<span class="sd">    Reference:</span>
<span class="sd">        A. Graves: Sequence Transduction with Recurrent Neural Networks:</span>
<span class="sd">        https://arxiv.org/abs/1211.3711.pdf</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
            <span class="bp">self</span><span class="p">,</span>
            <span class="n">configs</span><span class="p">:</span> <span class="n">DictConfig</span><span class="p">,</span>
            <span class="n">tokenizer</span><span class="p">:</span> <span class="n">Tokenizer</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="kn">from</span> <span class="nn">warp_rnnt</span> <span class="kn">import</span> <span class="n">rnnt_loss</span>
        <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="n">WARPRNNT_IMPORT_ERROR</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">rnnt_loss</span> <span class="o">=</span> <span class="n">rnnt_loss</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">blank_id</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">blank_id</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="n">configs</span><span class="o">.</span><span class="n">criterion</span><span class="o">.</span><span class="n">reduction</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">gather</span> <span class="o">=</span> <span class="n">configs</span><span class="o">.</span><span class="n">criterion</span><span class="o">.</span><span class="n">gather</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
            <span class="bp">self</span><span class="p">,</span>
            <span class="n">logits</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">,</span>
            <span class="n">targets</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">IntTensor</span><span class="p">,</span>
            <span class="n">input_lengths</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">IntTensor</span><span class="p">,</span>
            <span class="n">target_lengths</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">IntTensor</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">:</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnnt_loss</span><span class="p">(</span>
            <span class="n">logits</span><span class="p">,</span>
            <span class="n">targets</span><span class="p">,</span>
            <span class="n">input_lengths</span><span class="p">,</span>
            <span class="n">target_lengths</span><span class="p">,</span>
            <span class="n">reduction</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">reduction</span><span class="p">,</span>
            <span class="n">blank</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">blank_id</span><span class="p">,</span>
            <span class="n">gather</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">gather</span><span class="p">,</span>
        <span class="p">)</span></div>
</pre></div>

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